HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation

doi:10.1109/LGRS.2022.3147857

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation
	Cao, Yong1,2 ; Huo, Chunlei1,2 ; Xu, Nuo1,2 ; Zhang, Xin1,2 ; Xiang, Shiming1,2 ; Pan, Chunhong1,2
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
	2022
卷号	19 页码:5
通讯作者	Huo, Chunlei(clhuo@nlpria.ac.cn)
摘要	Semantic segmentation plays an important role in very high resolution (VHR) image understanding. Despite the potentials of the deep convolutional network in improving performance by end-to-end feature learning, each model has its limitations, and it is hard to discriminate complex features purely by a single model. Ensemble learning is promising for integrating the strengths of different models, however, the ensemble of deep models is challenging due to the huge amount of parameters and computation of the deep model itself as well as the difficulty in capturing complementarity between different models. To tackle these problems, a head-level ensemble network (HENet) is proposed in this letter, which reduces model complexity by sharing feature extraction networks and improves complementarity between models by novel cooperative learning (CL). Experiments on ISPRS 2-D semantic labeling benchmark demonstrate the effectiveness and advantage of the proposed method.
关键词	Head Computational modeling Semantics Image segmentation Feature extraction Correlation Mathematical models Cooperative learning (CL) ensemble learning semantic segmentation
DOI	10.1109/LGRS.2022.3147857
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0100400] ; Guangxi Natural Science Foundation[2018GXNSFBA281086] ; National Natural Science Foundation of China[62071466] ; National Natural Science Foundation of China[61802407]
项目资助者	National Key Research and Development Program of China ; Guangxi Natural Science Foundation ; National Natural Science Foundation of China
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000757847800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47910
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
通讯作者	Huo, Chunlei
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Cao, Yong,Huo, Chunlei,Xu, Nuo,et al. HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5.
APA	Cao, Yong,Huo, Chunlei,Xu, Nuo,Zhang, Xin,Xiang, Shiming,&Pan, Chunhong.(2022).HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5.
MLA	Cao, Yong,et al."HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5.